Hiring in Biotech is Tricky. But Algorithms Won’t Save the Day

Share and Comment

People who appreciate baseball stats agree: Jonny Gomes of the Boston Red Sox is a below-average player. Yet, if you pay attention to his intangibles, he looks better. Even with lousy performance data, he was credited with playing a key role last fall in helping his team win the World Series.

The Gomes example reminds me—no matter how hard people try—that you can’t reduce everything in the world to data-driven decision-making. Especially when you’re talking about evaluating people, and how they might perform for a company.

Biotech and pharma hiring managers, are you listening?

For a couple years now, I’ve been hearing biotech and pharma companies complain they can’t find enough good job candidates. At the same time, job seekers with excellent qualifications say they can’t get hired because they only check, say, 12 of the 15 or so qualification boxes on the application. One recent story in The New York Times noted that companies across multiple industries are becoming increasingly slow and picky in their hiring practices, and resorting to various gimmicks like spelling quizzes and math tests to filter the good candidates from the not-so-good. Biotech and pharma companies are the most cautious, with an average interview process that takes 29 days, the longest for any industry, according to a recent survey by the website Glassdoor.

What’s ironic here is that in the Internet age, it should be easier to match up job seekers with appropriate job openings, like with online dating. Instead, we are seeing companies craft job descriptions that no mere mortal can fulfill. Where does that leave the ambitious postdoc from academia with a great faculty advisor, a few publications, valuable expertise in something like oncology or neurology, and a positive attitude? Company X down the street may have a job that looks ideal, but oh, the candidate lacks 3-5 years of industry experience? Sorry, you just got filtered out in the online screening process.

The thing about algorithms is they can’t imagine how a talented person who lacks a few qualifications might be resourceful, and able to adapt and grow into a top performer if given a chance in the right environment.

Ellen Clark, scientific recruiter

Ellen Clark, a recruiter of senior scientists for biotech and pharma companies, said she’s noticed companies getting pickier about candidates in recent years. When they complain they can’t find talented people it’s “baloney,” she says. Often, there are talented people in academia looking for science-based jobs in industry, but companies are unwilling to consider anything but the “perfect” candidate.

“Sometimes they really want everything,” Clark says. “They want the person to walk on the moon. They really want the person who checks every single box. They want it all. I had one search, where the company wanted an MD/PhD with an oncology background AND genetics experience to bridge the gap between the research people and the clinical people. This is the kind of thing they are looking for. They are trying to find everything in one person.”

No doubt, companies have always felt like good help is hard to find. Big companies like Genentech, Genzyme, or Novartis get thousands of applications every month. Genentech alone says it gets 20,000 to 25,000 job applications a month, while it currently lists 532 job openings on its website. I don’t envy the people tasked with trying to filter through them all in a fair and efficient way. Certainly, I get why they fear making hiring mistakes, because it can be a long, painful, and toxic process to surgically remove a tumor, shall we say.

None of that excuses the kind of dysfunction that passes for business as usual in biotech and pharma hiring. Nick Corcodilos, a headhunter and job market columnist, summed up the cracks in the system when he commented on a related article I wrote last year:

We used to talk about people: chemists, biologists, scientists. Then HR started talking about “human resources,” and more recently about “assets.” A worker (at any level) is “talent.” But this game has now pushed even top executives into an incredibly reductionist view of recruiting and hiring: It’s all about database records. Renting them, buying them, searching them, filtering them, subjecting them to algorithms.

And the databases promise perfect hires, if HR will just search the records long enough and if managers will just wait patiently. Meanwhile, important work goes undone, and fantasies of “perfect candidates” yield complaints of talent shortages.

So what are job candidates supposed to do to navigate this algorithm-driven minefield? To find out, I followed up with Marie Beltran. I spoke with her almost a year ago, when she was unemployed after being laid off from a job in quality-control at Seattle-based Dendreon (NASDAQ: DNDN).

It took Beltran almost a year to find a job, but she ended up landing what sounds like a good gig as an associate scientist with Emergent Biosolutions in Seattle.

Beltran says she didn’t encounter the spelling quizzes or video games described in The New York Times article, but she did find a way around the online filters that probably would have immediately disqualified her from getting the job she got. She used an outplacement firm, got more serious about keeping her network fresh, and added some clever social media skills. (One of her tricks was to use LinkedIn regularly, and occasionally ‘like’ some of my Xconomy articles to stay in touch with me.)

Marie Beltran, associate scientist

“I had to get re-educated on how to approach the job hunt process. Job hunting, networking, and social media took on a new life, versus what I knew back in 2006,” Beltran said. “I did experience firsthand how important it is to know yourself and your skills set. One has to be succinct and sharp about marketing oneself, especially with recruiters. And yes, it takes a lot of patience with biotech firms. Either it goes into the abyss, processed into the ‘system’ or flat out rejection.”

I’m glad to hear that Beltran found gainful and challenging employment. It took a lot of patience and adaptability. She may not have checked every single box you could imagine for such a position, and I doubt that an algorithm would have pushed her application to the top of the stack.

But if you believe that people are more than a collection of data points, and that attitude is a crucial intangible—like with Jonny Gomes—then I suspect Beltran will be just fine. Someone had to make a subjective, human decision about her. That’s how it’s always been in the hiring game, and how it ought to be. At least, I suppose, until the machines can prove they are better at selecting people than actual people.

Companies need use common sense and figure out how to not tie themselves in knots with so-called ‘intelligent filtering’ online processes that consultant or software companies sell them. This is especially true for science, engineering or other technical roles. Hire quickly, onboard effectively and make sure that you move quickly if someone is not making it – a bad fit hurts both the company and the candidate. Finally, be brutal in measuring and assessing your own hiring practices.

http://www.asktheheadhunter.com/ Nick Corcodilos

I don’t agree, Nathaniel. Luke’s column is a thoughtful analysis of a big problem. Sullivan’s column is totally spurious. Virtually everything he writes in that column assumes an employer can know who the best hire is in advance. Sullivan’s commentary is called “selling the sizzle.” The root problem that Sullivan skirts is that his clients dump billions into automated recruiting (databases).

Sullivan’s assertions that Facebook and Google are the ones to watch are nuts. Just because two huge companies do something one way doesn’t mean that’s going to work for smaller companies. The comparison is gratuitous at best (though it does make for lots of words).

I’m sure there is plenty of blame to go around as to why it takes so long to get get hired in biotech. However, at the end of the day, it’s a highly specialized field, with skills in one related area not always directly translating to another. In my (biased, perhaps oversimiplified) opinion, it’s not like hiring an accountant. So, it’s no suprise it takes us a long time. Keep in mind the rough patch we’ve been through in the past 5 years. It is a monumental feat just to hire replacements for people who have left, let alone add new head count. Every position matters, and given that one wrong hire can really cause all sorts of long-lasting issues, there’s no need to rush.

not interested

The REALLY intelligent, adaptable “go getters” just walk away from this ridiculous industry. They will only deal direct with a hiring scientific manager. Most just hang up the phone when they find they are dealing with some ridiculous pompous HR idiot. The companies are managed by business types not scientists, while they tell investors they are all about research. The main reason for startup failure is the failure to attract and retain talent. These companies not only allow, but their HR actually fosters a sniping, “survivor game” culture in wich bullies and mockers prevail

not interested

99% of the people in these companies aren’t even worth letting in the door. The people who are hell bent on discovery and sucess don’t want to tow the line, or deal with red tape, they don’t have to! Good luck pharma/biotech, you are now only grabbing at straws. This is the main reason for “empty pipelines”.

Wilrski

The outcomes will always be subject to the size of the company, the marketplace, and how quickly they are looking to fill the position. For example, it is no surprise that larger companies want to find the perfect person to fill a position and are generally less adaptable to anything less. I think my recruiter friends call them purple unicorns or something to that effect. They are looking to fill a very specific void. As such, using algorithms supposedly ‘save’ time.

Larger companies have the bandwidth to take their time because they can justify spending the money and time during this process. However, if you look at it from a numbers standpoint, it is just not feasible for smaller companies. Hence, smaller companies tend to utilize their networks to get the job done – in an effort to lean the process, reduce HR misses, and retain talent in an effort to find versatile employees.

I believe all companies want the ‘intangibles’ but based on the sheer volume of resumes they get – it is hard to extract those qualities from a piece of paper.

I have been on the job market for about 4 months now, and I can safely say that when I look at positions posted – I do not even waste my time ‘interacting with a computer’ for the reasons stated in the article. I spend approximately 90% of my time networking (with humans) and learning about the company so that someone will refer me to a position or ask me to write in for a position (note: human interaction). Approaching the job search this way has yielded more leads and positive results than floating my resume into the abyss and being subjected to ‘fit algorithms’.

I believe people need to be creative with how they approach searching for a job, and educate themselves on the hidden job market rather than chasing their tail because they only interacted with their computer (note: non-human interaction). The comment on pg 2 about ‘re-educating oneself about the job market’ is a necessity because the landscape has significantly changed. Don Asher (Hidden Job Market) has a good handle on this.

Robert Jones

One thing missing in this discussion is the lack of training. The industry doesn’t seem to know what it needs. It knows what it wants, but not how to get it. To assemble a training program would require an understanding of what is needed. Since no on knows they turn to the hiring process, assuming they’ll be able to spot the properly skilled people.

Barbara Norwood

Luke, as one of Marie Beltran’s former supervisors, and one of her references – she is an exceptional employee, and Emergent is lucky to have her!